LEADER 03406nam 22006735 450 001 9910438057103321 005 20251202140541.0 010 $a9783642386527 010 $a3642386520 024 7 $a10.1007/978-3-642-38652-7 035 $a(OCoLC)847735622 035 $a(MiFhGG)GVRL6WIW 035 $a(CKB)2670000000371295 035 $a(MiAaPQ)EBC1317208 035 $a(MiFhGG)9783642386527 035 $a(DE-He213)978-3-642-38652-7 035 $a(EXLCZ)992670000000371295 100 $a20130531d2013 u| 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aDimensionality Reduction with Unsupervised Nearest Neighbors /$fby Oliver Kramer 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (xviii, 130 pages) $cillustrations (some color) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v51 300 $a"ISSN: 1868-4394." 311 08$a9783642386510 311 08$a3642386512 320 $aIncludes bibliographical references and index. 327 $aPart I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions. 330 $aThis book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.  . 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v51 606 $aEngineering mathematics 606 $aEngineering$xData processing 606 $aArtificial intelligence 606 $aOperations research 606 $aMathematical and Computational Engineering Applications 606 $aArtificial Intelligence 606 $aOperations Research and Decision Theory 615 0$aEngineering mathematics. 615 0$aEngineering$xData processing. 615 0$aArtificial intelligence. 615 0$aOperations research. 615 14$aMathematical and Computational Engineering Applications. 615 24$aArtificial Intelligence. 615 24$aOperations Research and Decision Theory. 676 $a006.31 676 $a519.5/36 700 $aKramer$b Oliver$0761919 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438057103321 996 $aDimensionality Reduction with Unsupervised Nearest Neighbors$92513616 997 $aUNINA